# Loading packages
library(tidyverse)
library(lubridate)
library(janitor)
library(skimr)
library(corrplot)
library(haven)
library(ggpubr)
library(sjlabelled)
library(sjPlot)

# Create new dataset
combined_data <- read_csv(here::here("Data/combined_final_data.csv"))

Initial Set of Model Analyses

No Pre-BMI adjustments, IEAA

## No Pre-BMI adjustments, IEAA
ieaa_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ ieaa_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

ieaa_pregresid_no_bmi_icc_length_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

ieaa_pregresid_no_bmi_icc_armcircm_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

ieaa_pregresid_no_bmi_icc_abdom_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

ieaa_pregresid_no_bmi_icc_headcirc_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

ieaa_pregresid_no_bmi_icc_total_skin_mean_home <- update(ieaa_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(ieaa_pregresid_no_bmi_icc_weight_home, ieaa_pregresid_no_bmi_icc_length_home, ieaa_pregresid_no_bmi_icc_armcircm_home, ieaa_pregresid_no_bmi_icc_abdom_mean_home, ieaa_pregresid_no_bmi_icc_headcirc_mean_home, ieaa_pregresid_no_bmi_icc_total_skin_mean_home)



No Pre-BMI adjustments, EEAA

## No Pre-BMI adjustments, EEAA
eeaa_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ eeaa_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

eeaa_pregresid_no_bmi_icc_length_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

eeaa_pregresid_no_bmi_icc_armcircm_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

eeaa_pregresid_no_bmi_icc_abdom_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

eeaa_pregresid_no_bmi_icc_headcirc_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

eeaa_pregresid_no_bmi_icc_total_skin_mean_home <- update(eeaa_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(eeaa_pregresid_no_bmi_icc_weight_home, eeaa_pregresid_no_bmi_icc_length_home, eeaa_pregresid_no_bmi_icc_armcircm_home, eeaa_pregresid_no_bmi_icc_abdom_mean_home, eeaa_pregresid_no_bmi_icc_headcirc_mean_home, eeaa_pregresid_no_bmi_icc_total_skin_mean_home)



No Pre-BMI adjustments, pheno_pregresidage

## No Pre-BMI adjustments, pheno_pregresidage
age_accel_pheno_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_pheno_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

age_accel_pheno_pregresid_no_bmi_icc_length_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

age_accel_pheno_pregresid_no_bmi_icc_armcircm_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

age_accel_pheno_pregresid_no_bmi_icc_abdom_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

age_accel_pheno_pregresid_no_bmi_icc_headcirc_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

age_accel_pheno_pregresid_no_bmi_icc_total_skin_mean_home <- update(age_accel_pheno_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(age_accel_pheno_pregresid_no_bmi_icc_weight_home, age_accel_pheno_pregresid_no_bmi_icc_length_home, age_accel_pheno_pregresid_no_bmi_icc_armcircm_home, age_accel_pheno_pregresid_no_bmi_icc_abdom_mean_home, age_accel_pheno_pregresid_no_bmi_icc_headcirc_mean_home, age_accel_pheno_pregresid_no_bmi_icc_total_skin_mean_home)



No Pre-BMI adjustments, Grimage

## No Pre-BMI adjustments, grimage
age_accel_grim_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_grim_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

age_accel_grim_pregresid_no_bmi_icc_length_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

age_accel_grim_pregresid_no_bmi_icc_armcircm_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

age_accel_grim_pregresid_no_bmi_icc_abdom_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

age_accel_grim_pregresid_no_bmi_icc_headcirc_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

age_accel_grim_pregresid_no_bmi_icc_total_skin_mean_home <- update(age_accel_grim_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(age_accel_grim_pregresid_no_bmi_icc_weight_home, age_accel_grim_pregresid_no_bmi_icc_length_home, age_accel_grim_pregresid_no_bmi_icc_armcircm_home, age_accel_grim_pregresid_no_bmi_icc_abdom_mean_home, age_accel_grim_pregresid_no_bmi_icc_headcirc_mean_home, age_accel_grim_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, IEAA

## Pre-BMI adjustments, IEAA
ieaa_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ ieaa_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

ieaa_pregresid_bmi_icc_length_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

ieaa_pregresid_bmi_icc_armcircm_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

ieaa_pregresid_bmi_icc_abdom_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

ieaa_pregresid_bmi_icc_headcirc_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

ieaa_pregresid_bmi_icc_total_skin_mean_home <- update(ieaa_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(ieaa_pregresid_bmi_icc_weight_home, ieaa_pregresid_bmi_icc_length_home, ieaa_pregresid_bmi_icc_armcircm_home, ieaa_pregresid_bmi_icc_abdom_mean_home, ieaa_pregresid_bmi_icc_headcirc_mean_home, ieaa_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S3_IEAA_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.25 – 0.06 2.68 0.008 -0.14 -0.30 – 0.02 16.79 <0.001 -0.02 -0.18 – 0.14 6.49 <0.001 -0.01 -0.16 – 0.15 10.71 <0.001 -0.21 -0.36 – -0.06 16.67 <0.001 0.10 -0.07 – 0.26 2.34 0.020
ieaa_pregresid 0.04 -0.06 – 0.15 0.82 0.415 0.07 -0.04 – 0.18 1.24 0.217 0.04 -0.07 – 0.15 0.77 0.442 0.00 -0.10 – 0.11 0.02 0.980 0.06 -0.04 – 0.16 1.17 0.241 0.03 -0.08 – 0.14 0.56 0.578
gestage 0.18 0.07 – 0.29 3.31 0.001 0.09 -0.02 – 0.20 1.68 0.094 0.20 0.09 – 0.32 3.61 <0.001 0.15 0.04 – 0.26 2.78 0.006 0.15 0.04 – 0.25 2.81 0.005 0.08 -0.04 – 0.19 1.35 0.180
measurement_age 0.20 0.09 – 0.32 3.52 0.001 0.27 0.16 – 0.39 4.69 <0.001 0.13 0.01 – 0.25 2.21 0.028 0.40 0.29 – 0.51 7.11 <0.001 0.28 0.17 – 0.39 5.02 <0.001 0.22 0.10 – 0.34 3.66 <0.001
iccsex [Male] 0.18 -0.03 – 0.40 1.67 0.096 0.27 0.05 – 0.49 2.44 0.015 0.04 -0.18 – 0.26 0.33 0.738 0.01 -0.20 – 0.23 0.14 0.892 0.40 0.19 – 0.61 3.81 <0.001 -0.18 -0.41 – 0.04 -1.59 0.113
ses_pc1 0.01 -0.11 – 0.12 0.13 0.898 0.03 -0.09 – 0.14 0.44 0.657 0.04 -0.08 – 0.15 0.66 0.512 -0.04 -0.15 – 0.07 -0.74 0.457 0.06 -0.05 – 0.17 1.08 0.280 0.03 -0.08 – 0.15 0.57 0.568
prebmiz 0.25 0.14 – 0.36 4.48 <0.001 0.14 0.03 – 0.25 2.54 0.012 0.22 0.11 – 0.33 3.93 <0.001 0.16 0.05 – 0.26 2.89 0.004 0.25 0.15 – 0.35 4.74 <0.001 0.13 0.02 – 0.25 2.32 0.021
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.139 / 0.121 0.127 / 0.109 0.103 / 0.084 0.178 / 0.161 0.214 / 0.198 0.073 / 0.054



Pre-BMI adjustments, eeaa_id

## Pre-BMI adjustments, EEAA
eeaa_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ eeaa_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

eeaa_pregresid_bmi_icc_length_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

eeaa_pregresid_bmi_icc_armcircm_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

eeaa_pregresid_bmi_icc_abdom_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

eeaa_pregresid_bmi_icc_headcirc_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

eeaa_pregresid_bmi_icc_total_skin_mean_home <- update(eeaa_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(eeaa_pregresid_bmi_icc_weight_home, eeaa_pregresid_bmi_icc_length_home, eeaa_pregresid_bmi_icc_armcircm_home, eeaa_pregresid_bmi_icc_abdom_mean_home, eeaa_pregresid_bmi_icc_headcirc_mean_home, eeaa_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S4_EEAA_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.06 2.65 0.009 -0.14 -0.30 – 0.02 16.73 <0.001 -0.02 -0.18 – 0.14 6.47 <0.001 -0.01 -0.16 – 0.15 10.73 <0.001 -0.21 -0.36 – -0.06 16.62 <0.001 0.10 -0.06 – 0.26 2.32 0.021
eeaa_pregresid -0.07 -0.18 – 0.04 -1.31 0.192 -0.05 -0.16 – 0.06 -0.88 0.378 0.04 -0.07 – 0.15 0.68 0.498 -0.06 -0.16 – 0.05 -1.07 0.285 -0.04 -0.15 – 0.06 -0.79 0.429 -0.04 -0.15 – 0.07 -0.73 0.468
gestage 0.19 0.08 – 0.29 3.34 0.001 0.10 -0.01 – 0.21 1.72 0.086 0.21 0.09 – 0.32 3.63 <0.001 0.15 0.04 – 0.26 2.79 0.006 0.15 0.05 – 0.26 2.85 0.005 0.08 -0.03 – 0.19 1.36 0.173
measurement_age 0.21 0.10 – 0.32 3.67 <0.001 0.28 0.17 – 0.40 4.83 <0.001 0.13 0.02 – 0.25 2.22 0.027 0.40 0.29 – 0.52 7.19 <0.001 0.28 0.18 – 0.39 5.15 <0.001 0.22 0.11 – 0.34 3.75 <0.001
iccsex [Male] 0.18 -0.04 – 0.39 1.61 0.108 0.26 0.04 – 0.48 2.37 0.019 0.04 -0.18 – 0.26 0.33 0.745 0.01 -0.20 – 0.22 0.11 0.915 0.39 0.19 – 0.60 3.74 <0.001 -0.19 -0.41 – 0.04 -1.63 0.104
ses_pc1 -0.00 -0.12 – 0.11 -0.04 0.970 0.02 -0.10 – 0.13 0.30 0.762 0.04 -0.08 – 0.16 0.69 0.490 -0.05 -0.16 – 0.06 -0.85 0.395 0.05 -0.06 – 0.16 0.95 0.344 0.03 -0.09 – 0.15 0.47 0.637
prebmiz 0.25 0.14 – 0.36 4.57 <0.001 0.14 0.03 – 0.25 2.60 0.010 0.22 0.11 – 0.33 3.87 <0.001 0.16 0.05 – 0.27 2.96 0.003 0.25 0.15 – 0.36 4.78 <0.001 0.14 0.02 – 0.25 2.37 0.018
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.142 / 0.124 0.125 / 0.107 0.102 / 0.084 0.181 / 0.164 0.212 / 0.196 0.074 / 0.055



Pre-BMI adjustments, phenoage

## Pre-BMI adjustments, phenoage
age_accel_pheno_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_pheno_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

age_accel_pheno_pregresid_bmi_icc_length_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

age_accel_pheno_pregresid_bmi_icc_armcircm_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

age_accel_pheno_pregresid_bmi_icc_abdom_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

age_accel_pheno_pregresid_bmi_icc_headcirc_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

age_accel_pheno_pregresid_bmi_icc_total_skin_mean_home <- update(age_accel_pheno_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(age_accel_pheno_pregresid_bmi_icc_weight_home, age_accel_pheno_pregresid_bmi_icc_length_home, age_accel_pheno_pregresid_bmi_icc_armcircm_home, age_accel_pheno_pregresid_bmi_icc_abdom_mean_home, age_accel_pheno_pregresid_bmi_icc_headcirc_mean_home, age_accel_pheno_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S5_pheno_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.07 2.66 0.008 -0.14 -0.29 – 0.02 16.73 <0.001 -0.02 -0.18 – 0.14 6.46 <0.001 -0.01 -0.16 – 0.15 10.71 <0.001 -0.21 -0.36 – -0.06 16.61 <0.001 0.10 -0.06 – 0.26 2.32 0.021
age_accel_pheno_pregresid -0.03 -0.14 – 0.08 -0.55 0.582 -0.03 -0.14 – 0.08 -0.53 0.600 0.02 -0.09 – 0.13 0.33 0.739 -0.00 -0.11 – 0.10 -0.09 0.926 -0.02 -0.12 – 0.09 -0.33 0.745 -0.01 -0.12 – 0.10 -0.20 0.841
gestage 0.18 0.08 – 0.29 3.33 0.001 0.10 -0.01 – 0.21 1.71 0.088 0.21 0.09 – 0.32 3.64 <0.001 0.15 0.04 – 0.26 2.78 0.006 0.15 0.05 – 0.26 2.84 0.005 0.08 -0.04 – 0.19 1.36 0.175
measurement_age 0.21 0.09 – 0.32 3.60 <0.001 0.28 0.16 – 0.39 4.79 <0.001 0.13 0.02 – 0.25 2.25 0.025 0.40 0.29 – 0.51 7.12 <0.001 0.28 0.17 – 0.39 5.12 <0.001 0.22 0.10 – 0.34 3.71 <0.001
iccsex [Male] 0.17 -0.05 – 0.39 1.56 0.120 0.26 0.04 – 0.48 2.31 0.022 0.04 -0.18 – 0.26 0.35 0.728 0.01 -0.20 – 0.23 0.12 0.903 0.39 0.18 – 0.60 3.69 <0.001 -0.19 -0.41 – 0.04 -1.62 0.106
ses_pc1 0.00 -0.11 – 0.12 0.07 0.941 0.02 -0.09 – 0.14 0.37 0.709 0.04 -0.08 – 0.15 0.64 0.524 -0.04 -0.15 – 0.07 -0.75 0.454 0.06 -0.05 – 0.16 1.02 0.309 0.03 -0.09 – 0.15 0.54 0.589
prebmiz 0.25 0.14 – 0.36 4.49 <0.001 0.14 0.03 – 0.25 2.55 0.011 0.22 0.11 – 0.33 3.92 <0.001 0.16 0.05 – 0.26 2.89 0.004 0.25 0.15 – 0.35 4.74 <0.001 0.13 0.02 – 0.25 2.33 0.021
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.138 / 0.120 0.124 / 0.105 0.101 / 0.082 0.178 / 0.161 0.211 / 0.194 0.073 / 0.053



Pre-BMI adjustments, grimage

## Pre-BMI adjustments, grimage
age_accel_grim_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ age_accel_grim_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

age_accel_grim_pregresid_bmi_icc_length_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

age_accel_grim_pregresid_bmi_icc_armcircm_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

age_accel_grim_pregresid_bmi_icc_abdom_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

age_accel_grim_pregresid_bmi_icc_headcirc_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

age_accel_grim_pregresid_bmi_icc_total_skin_mean_home <- update(age_accel_grim_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(age_accel_grim_pregresid_bmi_icc_weight_home, age_accel_grim_pregresid_bmi_icc_length_home, age_accel_grim_pregresid_bmi_icc_armcircm_home, age_accel_grim_pregresid_bmi_icc_abdom_mean_home, age_accel_grim_pregresid_bmi_icc_headcirc_mean_home, age_accel_grim_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S6_grim_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.26 – 0.05 2.63 0.009 -0.15 -0.31 – 0.01 16.78 <0.001 -0.02 -0.18 – 0.14 6.46 <0.001 -0.01 -0.16 – 0.14 10.71 <0.001 -0.22 -0.37 – -0.07 16.66 <0.001 0.09 -0.07 – 0.26 2.31 0.021
age_accel_grim_pregresid 0.08 -0.03 – 0.19 1.42 0.155 0.10 -0.01 – 0.21 1.84 0.068 0.05 -0.06 – 0.16 0.85 0.397 0.00 -0.10 – 0.11 0.08 0.935 0.09 -0.01 – 0.20 1.74 0.083 0.03 -0.08 – 0.14 0.53 0.597
gestage 0.19 0.08 – 0.30 3.37 0.001 0.10 -0.01 – 0.21 1.77 0.078 0.21 0.10 – 0.32 3.65 <0.001 0.15 0.04 – 0.26 2.78 0.006 0.15 0.05 – 0.26 2.89 0.004 0.08 -0.03 – 0.19 1.37 0.170
measurement_age 0.20 0.08 – 0.31 3.39 0.001 0.26 0.15 – 0.38 4.54 <0.001 0.13 0.01 – 0.24 2.15 0.033 0.40 0.29 – 0.51 7.06 <0.001 0.27 0.16 – 0.38 4.87 <0.001 0.22 0.10 – 0.34 3.61 <0.001
iccsex [Male] 0.20 -0.02 – 0.41 1.77 0.078 0.28 0.07 – 0.50 2.56 0.011 0.04 -0.18 – 0.27 0.38 0.702 0.02 -0.20 – 0.23 0.14 0.887 0.41 0.21 – 0.62 3.92 <0.001 -0.18 -0.40 – 0.05 -1.56 0.121
ses_pc1 0.01 -0.11 – 0.12 0.11 0.910 0.02 -0.09 – 0.14 0.42 0.676 0.04 -0.08 – 0.15 0.64 0.525 -0.04 -0.15 – 0.07 -0.75 0.457 0.06 -0.05 – 0.17 1.06 0.291 0.03 -0.08 – 0.15 0.56 0.579
prebmiz 0.24 0.13 – 0.35 4.28 <0.001 0.13 0.02 – 0.24 2.30 0.022 0.22 0.10 – 0.33 3.79 <0.001 0.16 0.05 – 0.26 2.85 0.005 0.24 0.13 – 0.34 4.50 <0.001 0.13 0.02 – 0.24 2.24 0.026
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.143 / 0.125 0.133 / 0.115 0.103 / 0.084 0.178 / 0.161 0.218 / 0.202 0.073 / 0.054

Overall conclusion: GrimAge looks the best, now going to try to dissect each of the individual "clocks" that make up GrimAge.

Secondary Set of Model Analyses (Focused on Components of GrimAge)

No Pre-BMI adjustments, dn_am_adm_adj_age

## No Pre-BMI adjustments, dn_am_adm_adj_age
dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_adm_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_adm_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_adm_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_adm_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_adm_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_adm_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_adm_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_length_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_adm_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_adm_adj_age


## Pre-BMI adjustments, dn_am_adm_adj_age
dn_am_adm_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_adm_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_adm_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_adm_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_adm_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_adm_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_adm_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_adm_adj_age_pregresid_bmi_icc_weight_home, dn_am_adm_adj_age_pregresid_bmi_icc_length_home, dn_am_adm_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_adm_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_adm_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_adm_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S7_adm_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.26 – 0.06 2.52 0.012 -0.15 -0.30 – 0.01 16.65 <0.001 -0.02 -0.18 – 0.14 6.36 <0.001 -0.01 -0.16 – 0.15 10.68 <0.001 -0.21 -0.36 – -0.06 16.50 <0.001 0.09 -0.07 – 0.26 2.26 0.025
dn_am_adm_adj_age_pregresid 0.09 -0.02 – 0.20 1.64 0.102 0.14 0.03 – 0.25 2.50 0.013 0.08 -0.03 – 0.19 1.40 0.162 -0.00 -0.11 – 0.10 -0.05 0.959 0.09 -0.01 – 0.19 1.72 0.086 0.04 -0.07 – 0.16 0.75 0.452
gestage 0.19 0.08 – 0.30 3.47 0.001 0.11 -0.00 – 0.22 1.93 0.055 0.21 0.10 – 0.32 3.74 <0.001 0.15 0.04 – 0.26 2.77 0.006 0.16 0.05 – 0.26 2.98 0.003 0.08 -0.03 – 0.20 1.42 0.157
measurement_age 0.20 0.09 – 0.32 3.56 <0.001 0.27 0.16 – 0.39 4.77 <0.001 0.13 0.02 – 0.25 2.24 0.026 0.40 0.29 – 0.51 7.13 <0.001 0.28 0.17 – 0.39 5.09 <0.001 0.22 0.10 – 0.34 3.69 <0.001
iccsex [Male] 0.19 -0.03 – 0.41 1.73 0.085 0.28 0.06 – 0.50 2.54 0.012 0.04 -0.18 – 0.26 0.38 0.706 0.01 -0.20 – 0.23 0.13 0.895 0.41 0.20 – 0.61 3.86 <0.001 -0.18 -0.40 – 0.05 -1.57 0.117
ses_pc1 -0.00 -0.11 – 0.11 -0.00 0.998 0.01 -0.10 – 0.13 0.25 0.805 0.03 -0.08 – 0.15 0.54 0.588 -0.04 -0.15 – 0.07 -0.74 0.459 0.05 -0.06 – 0.16 0.93 0.352 0.03 -0.09 – 0.15 0.50 0.615
prebmiz 0.24 0.13 – 0.35 4.34 <0.001 0.13 0.02 – 0.24 2.34 0.020 0.21 0.10 – 0.33 3.81 <0.001 0.16 0.05 – 0.26 2.88 0.004 0.24 0.14 – 0.35 4.59 <0.001 0.13 0.02 – 0.24 2.25 0.025
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.145 / 0.127 0.141 / 0.124 0.107 / 0.088 0.178 / 0.161 0.218 / 0.202 0.074 / 0.055



No Pre-BMI adjustments, dn_am_b2m_adj_age 

## No Pre-BMI adjustments, dn_am_b2m_adj_age
dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_b2m_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_b2m_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_b2m_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_b2m_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_b2m_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_b2m_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_b2m_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_length_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_b2m_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_b2m_adj_age


## Pre-BMI adjustments, dn_am_b2m_adj_age
dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_b2m_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_b2m_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_b2m_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_b2m_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_b2m_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_b2m_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_b2m_adj_age_pregresid_bmi_icc_weight_home, dn_am_b2m_adj_age_pregresid_bmi_icc_length_home, dn_am_b2m_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_b2m_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_b2m_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_b2m_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S8_b2m_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.06 2.65 0.008 -0.14 -0.30 – 0.02 16.71 <0.001 -0.02 -0.18 – 0.14 6.45 <0.001 -0.01 -0.16 – 0.14 10.72 <0.001 -0.21 -0.36 – -0.06 16.64 <0.001 0.10 -0.07 – 0.26 2.31 0.022
dn_am_b2m_adj_age_pregresid -0.02 -0.12 – 0.09 -0.31 0.757 0.02 -0.09 – 0.13 0.38 0.706 0.04 -0.07 – 0.15 0.79 0.433 -0.02 -0.13 – 0.08 -0.42 0.675 -0.05 -0.15 – 0.06 -0.87 0.383 0.04 -0.07 – 0.15 0.69 0.492
gestage 0.18 0.08 – 0.29 3.33 0.001 0.10 -0.01 – 0.21 1.73 0.085 0.21 0.10 – 0.32 3.65 <0.001 0.15 0.04 – 0.26 2.77 0.006 0.15 0.05 – 0.25 2.82 0.005 0.08 -0.03 – 0.19 1.38 0.168
measurement_age 0.21 0.09 – 0.32 3.59 <0.001 0.28 0.16 – 0.39 4.77 <0.001 0.13 0.02 – 0.25 2.26 0.025 0.40 0.29 – 0.51 7.13 <0.001 0.28 0.17 – 0.39 5.12 <0.001 0.22 0.10 – 0.34 3.70 <0.001
iccsex [Male] 0.18 -0.04 – 0.40 1.64 0.102 0.27 0.05 – 0.48 2.39 0.018 0.03 -0.19 – 0.26 0.31 0.760 0.01 -0.20 – 0.23 0.14 0.892 0.40 0.19 – 0.60 3.77 <0.001 -0.18 -0.41 – 0.04 -1.61 0.108
ses_pc1 0.00 -0.11 – 0.12 0.08 0.940 0.02 -0.09 – 0.14 0.42 0.676 0.04 -0.08 – 0.16 0.68 0.500 -0.04 -0.15 – 0.07 -0.77 0.441 0.05 -0.05 – 0.16 0.97 0.330 0.04 -0.08 – 0.15 0.59 0.554
prebmiz 0.25 0.14 – 0.36 4.49 <0.001 0.14 0.03 – 0.25 2.52 0.012 0.22 0.11 – 0.33 3.90 <0.001 0.16 0.05 – 0.26 2.90 0.004 0.25 0.15 – 0.36 4.76 <0.001 0.13 0.02 – 0.24 2.30 0.022
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.137 / 0.120 0.123 / 0.105 0.103 / 0.084 0.178 / 0.161 0.212 / 0.196 0.074 / 0.055



No Pre-BMI adjustments, dn_am_cystatin_c_adj_age 

## No Pre-BMI adjustments, dn_am_cystatin_c_adj_age
dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_cystatin_c_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_length_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_cystatin_c_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_cystatin_c_adj_age


## Pre-BMI adjustments, dn_am_cystatin_c_adj_age
dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_cystatin_c_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_cystatin_c_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_cystatin_c_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_cystatin_c_adj_age_pregresid_bmi_icc_weight_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_length_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_cystatin_c_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S9_cystatin_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.25 – 0.06 2.62 0.009 -0.14 -0.30 – 0.01 16.70 <0.001 -0.02 -0.18 – 0.14 6.45 <0.001 -0.01 -0.16 – 0.14 10.69 <0.001 -0.21 -0.36 – -0.06 16.58 <0.001 0.09 -0.07 – 0.25 2.28 0.024
dn_am_cystatin_c_adj_age_pregresid 0.03 -0.07 – 0.14 0.63 0.532 0.07 -0.04 – 0.18 1.29 0.197 0.01 -0.10 – 0.12 0.19 0.852 0.03 -0.07 – 0.14 0.61 0.542 0.06 -0.05 – 0.16 1.09 0.275 0.07 -0.04 – 0.18 1.20 0.230
gestage 0.19 0.08 – 0.30 3.36 0.001 0.10 -0.01 – 0.21 1.78 0.077 0.21 0.09 – 0.32 3.63 <0.001 0.15 0.05 – 0.26 2.81 0.005 0.15 0.05 – 0.26 2.89 0.004 0.08 -0.03 – 0.19 1.42 0.158
measurement_age 0.20 0.09 – 0.32 3.53 <0.001 0.27 0.16 – 0.39 4.69 <0.001 0.13 0.02 – 0.25 2.25 0.025 0.40 0.29 – 0.51 7.07 <0.001 0.28 0.17 – 0.39 5.03 <0.001 0.22 0.10 – 0.33 3.63 <0.001
iccsex [Male] 0.18 -0.03 – 0.40 1.67 0.096 0.27 0.05 – 0.49 2.45 0.015 0.04 -0.19 – 0.26 0.32 0.752 0.02 -0.19 – 0.23 0.16 0.871 0.40 0.19 – 0.61 3.82 <0.001 -0.18 -0.40 – 0.05 -1.56 0.120
ses_pc1 0.01 -0.11 – 0.12 0.14 0.887 0.03 -0.09 – 0.14 0.49 0.624 0.04 -0.08 – 0.15 0.64 0.524 -0.04 -0.15 – 0.07 -0.70 0.485 0.06 -0.05 – 0.17 1.11 0.267 0.04 -0.08 – 0.16 0.64 0.524
prebmiz 0.25 0.14 – 0.36 4.49 <0.001 0.14 0.03 – 0.25 2.56 0.011 0.22 0.11 – 0.33 3.93 <0.001 0.16 0.05 – 0.26 2.90 0.004 0.25 0.15 – 0.35 4.76 <0.001 0.13 0.02 – 0.25 2.35 0.020
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.138 / 0.120 0.128 / 0.110 0.101 / 0.082 0.179 / 0.162 0.214 / 0.197 0.077 / 0.058



No Pre-BMI adjustments, dn_am_gdf15adj_age_pregresid 

## No Pre-BMI adjustments, dn_am_gdf15adj_age
dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_gdf15adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_gdf15adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_gdf15adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_gdf15adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_gdf15adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_gdf15adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_gdf15adj_age_pregresid_no_bmi_icc_weight_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_length_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_gdf15adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_gdf15adj_age


## Pre-BMI adjustments, dn_am_gdf15adj_age
dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_gdf15adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_gdf15adj_age_pregresid_bmi_icc_length_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_gdf15adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_gdf15adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_gdf15adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_gdf15adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_gdf15adj_age_pregresid_bmi_icc_weight_home, dn_am_gdf15adj_age_pregresid_bmi_icc_length_home, dn_am_gdf15adj_age_pregresid_bmi_icc_armcircm_home, dn_am_gdf15adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_gdf15adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_gdf15adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S10_gdf15_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.06 2.65 0.009 -0.14 -0.30 – 0.02 16.74 <0.001 -0.02 -0.18 – 0.14 6.46 <0.001 -0.01 -0.16 – 0.14 10.71 <0.001 -0.21 -0.36 – -0.06 16.66 <0.001 0.10 -0.07 – 0.26 2.32 0.021
dn_am_gdf15adj_age_pregresid 0.01 -0.10 – 0.12 0.23 0.819 0.05 -0.06 – 0.15 0.83 0.409 -0.02 -0.13 – 0.09 -0.36 0.722 -0.05 -0.15 – 0.06 -0.91 0.363 0.06 -0.04 – 0.17 1.20 0.229 0.01 -0.10 – 0.13 0.24 0.809
gestage 0.19 0.08 – 0.29 3.33 0.001 0.10 -0.01 – 0.21 1.71 0.088 0.21 0.09 – 0.32 3.63 <0.001 0.15 0.04 – 0.26 2.80 0.006 0.15 0.05 – 0.25 2.83 0.005 0.08 -0.04 – 0.19 1.36 0.175
measurement_age 0.21 0.09 – 0.32 3.57 <0.001 0.28 0.16 – 0.39 4.75 <0.001 0.13 0.02 – 0.25 2.28 0.023 0.40 0.29 – 0.51 7.17 <0.001 0.28 0.17 – 0.39 5.07 <0.001 0.22 0.10 – 0.34 3.70 <0.001
iccsex [Male] 0.18 -0.04 – 0.40 1.64 0.102 0.27 0.05 – 0.48 2.39 0.017 0.03 -0.19 – 0.26 0.31 0.759 0.01 -0.20 – 0.23 0.13 0.893 0.40 0.19 – 0.60 3.77 <0.001 -0.18 -0.41 – 0.04 -1.61 0.109
ses_pc1 0.01 -0.11 – 0.12 0.11 0.909 0.03 -0.09 – 0.14 0.46 0.646 0.03 -0.08 – 0.15 0.59 0.553 -0.05 -0.16 – 0.06 -0.82 0.414 0.06 -0.05 – 0.17 1.13 0.260 0.03 -0.08 – 0.15 0.57 0.571
prebmiz 0.25 0.14 – 0.36 4.48 <0.001 0.14 0.03 – 0.25 2.58 0.010 0.22 0.11 – 0.33 3.90 <0.001 0.15 0.05 – 0.26 2.84 0.005 0.25 0.15 – 0.36 4.80 <0.001 0.13 0.02 – 0.25 2.33 0.020
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.137 / 0.119 0.125 / 0.107 0.101 / 0.083 0.180 / 0.163 0.214 / 0.198 0.073 / 0.053



No Pre-BMI adjustments, dn_am_leptin_adj_age_pregresid 

## No Pre-BMI adjustments, dn_am_leptin_adj_age
dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_leptin_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_leptin_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_leptin_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_leptin_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_leptin_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_leptin_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_leptin_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_length_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_leptin_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_leptin_adj_age


## Pre-BMI adjustments, dn_am_leptin_adj_age
dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_leptin_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_leptin_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_leptin_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_leptin_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_leptin_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_leptin_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_leptin_adj_age_pregresid_bmi_icc_weight_home, dn_am_leptin_adj_age_pregresid_bmi_icc_length_home, dn_am_leptin_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_leptin_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_leptin_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_leptin_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S11_leptin_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.25 – 0.06 2.50 0.013 -0.14 -0.30 – 0.02 16.49 <0.001 -0.02 -0.18 – 0.14 6.46 <0.001 -0.01 -0.16 – 0.14 10.41 <0.001 -0.21 -0.36 – -0.06 16.25 <0.001 0.09 -0.07 – 0.26 2.15 0.032
dn_am_leptin_adj_age_pregresid 0.04 -0.07 – 0.15 0.74 0.461 0.01 -0.10 – 0.12 0.19 0.851 -0.03 -0.14 – 0.08 -0.49 0.625 0.08 -0.03 – 0.18 1.40 0.162 0.07 -0.03 – 0.18 1.39 0.166 0.05 -0.06 – 0.17 0.96 0.339
gestage 0.19 0.08 – 0.30 3.41 0.001 0.10 -0.01 – 0.21 1.73 0.085 0.20 0.09 – 0.31 3.51 0.001 0.16 0.05 – 0.27 2.97 0.003 0.16 0.06 – 0.27 3.03 0.003 0.09 -0.03 – 0.20 1.50 0.136
measurement_age 0.21 0.09 – 0.32 3.59 <0.001 0.28 0.16 – 0.39 4.78 <0.001 0.13 0.02 – 0.25 2.26 0.024 0.40 0.29 – 0.51 7.16 <0.001 0.28 0.17 – 0.39 5.13 <0.001 0.22 0.10 – 0.34 3.72 <0.001
iccsex [Male] 0.18 -0.03 – 0.40 1.66 0.098 0.27 0.05 – 0.49 2.39 0.017 0.03 -0.19 – 0.25 0.29 0.768 0.02 -0.19 – 0.23 0.17 0.863 0.40 0.19 – 0.61 3.81 <0.001 -0.18 -0.41 – 0.04 -1.59 0.114
ses_pc1 0.01 -0.11 – 0.12 0.13 0.896 0.02 -0.09 – 0.14 0.40 0.688 0.04 -0.08 – 0.15 0.60 0.548 -0.04 -0.15 – 0.07 -0.68 0.497 0.06 -0.05 – 0.17 1.10 0.271 0.04 -0.08 – 0.15 0.60 0.552
prebmiz 0.25 0.14 – 0.35 4.44 <0.001 0.14 0.03 – 0.25 2.52 0.012 0.22 0.11 – 0.33 3.94 <0.001 0.15 0.05 – 0.26 2.83 0.005 0.25 0.14 – 0.35 4.68 <0.001 0.13 0.02 – 0.24 2.28 0.023
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.139 / 0.121 0.123 / 0.105 0.102 / 0.083 0.183 / 0.166 0.216 / 0.199 0.075 / 0.056



No Pre-BMI adjustments, dn_am_packyrs_adj_age 

## No Pre-BMI adjustments, dn_am_packyrs_adj_age
dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_packyrs_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_packyrs_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_packyrs_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_packyrs_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_packyrs_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_packyrs_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_packyrs_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_length_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_packyrs_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_packyrs_adj_age_pregresid


## Pre-BMI adjustments, dn_am_packyrs_adj_age
dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_packyrs_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_packyrs_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_packyrs_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_packyrs_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_packyrs_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_packyrs_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_packyrs_adj_age_pregresid_bmi_icc_weight_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_length_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_packyrs_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S12_packyears_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.26 – 0.06 2.66 0.008 -0.14 -0.30 – 0.02 16.71 <0.001 -0.02 -0.18 – 0.14 6.47 <0.001 -0.01 -0.16 – 0.14 10.72 <0.001 -0.21 -0.36 – -0.06 16.65 <0.001 0.10 -0.06 – 0.26 2.32 0.021
dn_am_packyrs_adj_age_pregresid 0.06 -0.05 – 0.17 1.09 0.278 -0.00 -0.11 – 0.11 -0.07 0.948 0.01 -0.10 – 0.13 0.26 0.798 0.02 -0.08 – 0.13 0.41 0.679 0.06 -0.04 – 0.17 1.16 0.248 -0.01 -0.13 – 0.10 -0.22 0.823
gestage 0.19 0.08 – 0.29 3.34 0.001 0.10 -0.01 – 0.21 1.72 0.087 0.21 0.09 – 0.32 3.63 <0.001 0.15 0.04 – 0.26 2.78 0.006 0.15 0.05 – 0.26 2.85 0.005 0.08 -0.03 – 0.19 1.36 0.174
measurement_age 0.20 0.08 – 0.31 3.41 0.001 0.28 0.16 – 0.39 4.74 <0.001 0.13 0.01 – 0.25 2.21 0.028 0.40 0.29 – 0.51 7.00 <0.001 0.27 0.16 – 0.38 4.91 <0.001 0.22 0.10 – 0.34 3.70 <0.001
iccsex [Male] 0.19 -0.03 – 0.41 1.72 0.086 0.27 0.05 – 0.48 2.38 0.018 0.04 -0.19 – 0.26 0.33 0.745 0.02 -0.19 – 0.23 0.17 0.869 0.41 0.20 – 0.61 3.85 <0.001 -0.19 -0.41 – 0.04 -1.62 0.106
ses_pc1 0.01 -0.10 – 0.12 0.16 0.875 0.02 -0.09 – 0.14 0.39 0.697 0.04 -0.08 – 0.15 0.64 0.523 -0.04 -0.15 – 0.07 -0.72 0.471 0.06 -0.05 – 0.17 1.10 0.273 0.03 -0.09 – 0.15 0.54 0.593
prebmiz 0.24 0.13 – 0.35 4.38 <0.001 0.14 0.03 – 0.25 2.53 0.012 0.22 0.11 – 0.33 3.89 <0.001 0.15 0.05 – 0.26 2.84 0.005 0.25 0.14 – 0.35 4.63 <0.001 0.13 0.02 – 0.25 2.34 0.020
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.141 / 0.123 0.123 / 0.105 0.101 / 0.082 0.178 / 0.161 0.214 / 0.198 0.073 / 0.053



No Pre-BMI adjustments, dn_am_pai1adj_age 

## No Pre-BMI adjustments, dn_am_pai1adj_age
dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_pai1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_pai1adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_pai1adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_pai1adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_pai1adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_pai1adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_pai1adj_age_pregresid_no_bmi_icc_weight_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_length_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_pai1adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_pai1adj_age


## Pre-BMI adjustments, dn_am_pai1adj_age
dn_am_pai1adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_pai1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_pai1adj_age_pregresid_bmi_icc_length_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_pai1adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_pai1adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_pai1adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_pai1adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_pai1adj_age_pregresid_bmi_icc_weight_home, dn_am_pai1adj_age_pregresid_bmi_icc_length_home, dn_am_pai1adj_age_pregresid_bmi_icc_armcircm_home, dn_am_pai1adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_pai1adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_pai1adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S13_pai1_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.06 2.65 0.009 -0.14 -0.30 – 0.02 16.75 <0.001 -0.02 -0.18 – 0.14 6.44 <0.001 -0.01 -0.16 – 0.15 10.67 <0.001 -0.21 -0.36 – -0.06 16.59 <0.001 0.10 -0.07 – 0.26 2.31 0.021
dn_am_pai1adj_age_pregresid 0.01 -0.11 – 0.12 0.09 0.929 0.04 -0.07 – 0.16 0.73 0.464 -0.02 -0.13 – 0.10 -0.29 0.774 -0.06 -0.17 – 0.06 -0.97 0.332 0.01 -0.10 – 0.12 0.21 0.833 -0.01 -0.13 – 0.11 -0.11 0.914
gestage 0.19 0.08 – 0.29 3.33 0.001 0.09 -0.02 – 0.21 1.69 0.092 0.21 0.09 – 0.32 3.64 <0.001 0.15 0.05 – 0.26 2.82 0.005 0.15 0.05 – 0.26 2.83 0.005 0.08 -0.03 – 0.19 1.37 0.173
measurement_age 0.21 0.09 – 0.32 3.55 <0.001 0.27 0.16 – 0.39 4.67 <0.001 0.14 0.02 – 0.25 2.29 0.023 0.41 0.30 – 0.52 7.20 <0.001 0.28 0.17 – 0.39 5.05 <0.001 0.22 0.10 – 0.34 3.70 <0.001
iccsex [Male] 0.18 -0.04 – 0.40 1.64 0.101 0.27 0.05 – 0.49 2.42 0.016 0.03 -0.19 – 0.26 0.30 0.768 0.01 -0.20 – 0.22 0.09 0.925 0.40 0.19 – 0.61 3.77 <0.001 -0.18 -0.41 – 0.04 -1.61 0.108
ses_pc1 0.00 -0.11 – 0.12 0.07 0.941 0.01 -0.10 – 0.13 0.22 0.823 0.04 -0.08 – 0.16 0.67 0.501 -0.03 -0.14 – 0.08 -0.52 0.607 0.05 -0.06 – 0.17 0.96 0.338 0.03 -0.09 – 0.15 0.56 0.577
prebmiz 0.25 0.13 – 0.36 4.31 <0.001 0.13 0.02 – 0.24 2.27 0.024 0.23 0.11 – 0.34 3.87 <0.001 0.17 0.06 – 0.28 3.04 0.003 0.25 0.14 – 0.35 4.53 <0.001 0.13 0.02 – 0.25 2.28 0.024
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.137 / 0.119 0.124 / 0.106 0.101 / 0.082 0.181 / 0.163 0.210 / 0.194 0.072 / 0.053



No Pre-BMI adjustments, dn_am_timp1adj_age 

## No Pre-BMI adjustments, dn_am_timp1adj_age
dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_timp1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_timp1adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_timp1adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_timp1adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_timp1adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_timp1adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_timp1adj_age_pregresid_no_bmi_icc_weight_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_length_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_timp1adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_timp1adj_age


## Pre-BMI adjustments, dn_am_timp1adj_age
dn_am_timp1adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_timp1adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_timp1adj_age_pregresid_bmi_icc_length_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_timp1adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_timp1adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_timp1adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_timp1adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_timp1adj_age_pregresid_bmi_icc_weight_home, dn_am_timp1adj_age_pregresid_bmi_icc_length_home, dn_am_timp1adj_age_pregresid_bmi_icc_armcircm_home, dn_am_timp1adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_timp1adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_timp1adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S14_timp1_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.10 -0.25 – 0.06 2.65 0.008 -0.15 -0.30 – 0.01 16.79 <0.001 -0.02 -0.18 – 0.14 6.47 <0.001 -0.01 -0.16 – 0.14 10.73 <0.001 -0.21 -0.36 – -0.06 16.64 <0.001 0.10 -0.07 – 0.26 2.32 0.021
dn_am_timp1adj_age_pregresid 0.04 -0.07 – 0.15 0.71 0.479 0.09 -0.02 – 0.20 1.57 0.118 0.04 -0.07 – 0.15 0.63 0.526 0.05 -0.05 – 0.16 0.96 0.337 0.06 -0.05 – 0.16 1.06 0.289 0.01 -0.10 – 0.13 0.24 0.814
gestage 0.19 0.08 – 0.29 3.33 0.001 0.10 -0.01 – 0.21 1.72 0.087 0.21 0.09 – 0.32 3.63 <0.001 0.15 0.04 – 0.26 2.78 0.006 0.15 0.05 – 0.26 2.84 0.005 0.08 -0.03 – 0.19 1.36 0.174
measurement_age 0.21 0.09 – 0.32 3.57 <0.001 0.28 0.16 – 0.39 4.77 <0.001 0.13 0.02 – 0.25 2.26 0.025 0.40 0.29 – 0.51 7.12 <0.001 0.28 0.17 – 0.39 5.10 <0.001 0.22 0.10 – 0.34 3.70 <0.001
iccsex [Male] 0.19 -0.03 – 0.40 1.69 0.091 0.28 0.06 – 0.50 2.52 0.012 0.04 -0.18 – 0.26 0.36 0.721 0.02 -0.19 – 0.23 0.21 0.833 0.41 0.20 – 0.61 3.84 <0.001 -0.18 -0.41 – 0.04 -1.59 0.114
ses_pc1 0.01 -0.11 – 0.12 0.14 0.887 0.03 -0.09 – 0.14 0.50 0.619 0.04 -0.08 – 0.15 0.67 0.506 -0.04 -0.15 – 0.07 -0.68 0.495 0.06 -0.05 – 0.17 1.10 0.272 0.03 -0.08 – 0.15 0.56 0.574
prebmiz 0.24 0.14 – 0.35 4.42 <0.001 0.14 0.03 – 0.24 2.44 0.015 0.22 0.11 – 0.33 3.88 <0.001 0.15 0.05 – 0.26 2.82 0.005 0.25 0.14 – 0.35 4.66 <0.001 0.13 0.02 – 0.25 2.30 0.022
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.139 / 0.121 0.130 / 0.112 0.102 / 0.083 0.180 / 0.163 0.213 / 0.197 0.073 / 0.053



No Pre-BMI adjustments, dn_am_tl_adj_age 

## No Pre-BMI adjustments, dn_am_tl_adj_age
dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_tl_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1,data = combined_data)

dn_am_tl_adj_age_pregresid_no_bmi_icc_length_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_tl_adj_age_pregresid_no_bmi_icc_armcircm_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_tl_adj_age_pregresid_no_bmi_icc_abdom_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_tl_adj_age_pregresid_no_bmi_icc_headcirc_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_tl_adj_age_pregresid_no_bmi_icc_total_skin_mean_home <- update(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_tl_adj_age_pregresid_no_bmi_icc_weight_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_length_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_armcircm_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_abdom_mean_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_headcirc_mean_home, dn_am_tl_adj_age_pregresid_no_bmi_icc_total_skin_mean_home)



Pre-BMI adjustments, dn_am_tl_adj_age


## Pre-BMI adjustments, dn_am_tl_adj_age
dn_am_tl_adj_age_pregresid_bmi_icc_weight_home <- lm(icc_weight_home ~ dn_am_tl_adj_age_pregresid + gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

dn_am_tl_adj_age_pregresid_bmi_icc_length_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_length_home ~ .)

dn_am_tl_adj_age_pregresid_bmi_icc_armcircm_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_armcircm_home ~ .)

dn_am_tl_adj_age_pregresid_bmi_icc_abdom_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_abdom_mean_home ~ .)

dn_am_tl_adj_age_pregresid_bmi_icc_headcirc_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_headcirc_mean_home ~ .)

dn_am_tl_adj_age_pregresid_bmi_icc_total_skin_mean_home <- update(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, icc_total_skin_mean_home ~ .)

sjPlot::tab_model(dn_am_tl_adj_age_pregresid_bmi_icc_weight_home, dn_am_tl_adj_age_pregresid_bmi_icc_length_home, dn_am_tl_adj_age_pregresid_bmi_icc_armcircm_home, dn_am_tl_adj_age_pregresid_bmi_icc_abdom_mean_home, dn_am_tl_adj_age_pregresid_bmi_icc_headcirc_mean_home, dn_am_tl_adj_age_pregresid_bmi_icc_total_skin_mean_home,  show.est = FALSE, show.se = FALSE, show.stat = TRUE, show.std = TRUE, file = here::here("Output/Figures", "Table S15_tl_other.doc"))
  icc weight home icc length home icc armcircm home icc abdom mean home icc headcirc mean home icc total skin mean home
Predictors std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p std. Beta standardized CI Statistic p
(Intercept) -0.09 -0.25 – 0.07 2.61 0.010 -0.13 -0.29 – 0.03 16.76 <0.001 -0.02 -0.18 – 0.14 6.47 <0.001 -0.00 -0.16 – 0.15 10.68 <0.001 -0.20 -0.35 – -0.05 16.58 <0.001 0.10 -0.07 – 0.26 2.32 0.021
dn_am_tl_adj_age_pregresid 0.05 -0.06 – 0.16 0.96 0.339 0.13 0.02 – 0.23 2.30 0.022 -0.02 -0.13 – 0.09 -0.38 0.702 0.05 -0.05 – 0.16 0.97 0.331 0.06 -0.04 – 0.16 1.16 0.247 0.01 -0.11 – 0.12 0.09 0.930
gestage 0.19 0.08 – 0.30 3.38 0.001 0.10 -0.01 – 0.21 1.84 0.067 0.21 0.09 – 0.32 3.61 <0.001 0.15 0.05 – 0.26 2.83 0.005 0.15 0.05 – 0.26 2.90 0.004 0.08 -0.03 – 0.19 1.37 0.173
measurement_age 0.21 0.09 – 0.32 3.57 <0.001 0.27 0.16 – 0.39 4.77 <0.001 0.13 0.02 – 0.25 2.28 0.024 0.40 0.29 – 0.51 7.12 <0.001 0.28 0.17 – 0.39 5.09 <0.001 0.22 0.10 – 0.34 3.71 <0.001
iccsex [Male] 0.17 -0.04 – 0.39 1.56 0.119 0.25 0.03 – 0.46 2.22 0.027 0.04 -0.18 – 0.26 0.34 0.736 0.01 -0.21 – 0.22 0.06 0.954 0.39 0.18 – 0.60 3.67 <0.001 -0.18 -0.41 – 0.04 -1.61 0.108
ses_pc1 0.00 -0.11 – 0.12 0.06 0.950 0.02 -0.09 – 0.13 0.32 0.749 0.04 -0.08 – 0.15 0.64 0.524 -0.04 -0.15 – 0.07 -0.78 0.436 0.05 -0.05 – 0.16 0.99 0.321 0.03 -0.08 – 0.15 0.55 0.586
prebmiz 0.24 0.14 – 0.35 4.42 <0.001 0.13 0.02 – 0.24 2.41 0.017 0.22 0.11 – 0.33 3.94 <0.001 0.15 0.05 – 0.26 2.82 0.005 0.25 0.14 – 0.35 4.66 <0.001 0.13 0.02 – 0.25 2.31 0.021
Observations 296 296 296 296 296 296
R2 / R2 adjusted 0.140 / 0.122 0.139 / 0.121 0.101 / 0.083 0.181 / 0.164 0.214 / 0.198 0.072 / 0.053

Figures

Leptin and head circumference

Get residuals

# Get residuals
# 
length_basic <- lm(icc_length_home ~ gestage + measurement_age + iccsex + ses_pc1 + prebmiz,data = combined_data)

headcirc_basic <-update(length_basic, icc_headcirc_mean_home ~ . )

weight_basic <-update(length_basic, icc_weight_home ~ . )

length_resids <-broom::augment(length_basic)$.resid
headcirc_resids <-broom::augment(headcirc_basic)$.resid
weight_resids <-broom::augment(weight_basic)$.resid

Make figures

all_but_leptin_gest_age_no_meas <- lm(gestage ~ iccsex + ses_pc1 + prebmiz ,data = combined_data)

ga_resids <-broom::augment(all_but_leptin_gest_age_no_meas)$.resid



leptin_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_leptin_adj_age_pregresid, y = ga_resids)) +
  geom_point(color = 'firebrick', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmLeptin EEA", y = "Postnatal Measured Weight (residuals)")+
  theme_pubr()+
  annotate("text", x = -2800, y = -50, label=expression(beta *  "= -0.15,  p-value = 0.009")); leptin_plot

adm_weight_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = weight_resids)) +
  geom_point(color = 'steelblue', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmADM EEA", y = "Postnatal Measured Weight (residuals)")+
  theme_pubr()+
  annotate("text", x = -28, y = -1.3, label=expression(beta *  "= 0.09,  p-value = 0.102")); adm_weight_plot

adm_length_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = length_resids)) +
  geom_point(color = 'steelblue', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmADM EEA", y = "Postnatal Length (residuals)")+
  theme_pubr()+
    annotate("text", x = -28, y = -10, label=expression(beta *  "= 0.14,  p-value = 0.013")); adm_length_plot

adm_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_adm_adj_age_pregresid, y = headcirc_resids)) +
  geom_point(color = 'steelblue', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmADM EEA", y = "Postnatal Head Circumference (residuals)")+
  theme_pubr()+
  annotate("text", x = -28, y = -4.5, label=expression(beta *  "= 0.09,  p-value = 0.086")); adm_headcirc_plot

ggarrange(leptin_plot, adm_length_plot, adm_headcirc_plot, adm_weight_plot,  
          labels = c("A", "B", "C", "D"), 
          ncol = 2, nrow = 2)

Get Grim and TL residuals

# Get residuals
length_resids <-broom::augment(length_basic)$.resid
headcirc_resids <-broom::augment(headcirc_basic)$.resid
TL_headcirc_plot <-broom::augment(headcirc_basic)$.resid

grim_length_plot <-ggplot(data = combined_data, mapping = aes(x = age_accel_grim_pregresid, y = length_resids)) +
  geom_point(color = 'purple', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmGrim EEA", y = "Postnatal Length (residuals)")+
  theme_pubr()+
    annotate("text", x = -3.5, y = -10, label=expression(beta *  "= 0.14,  p-value = 0.013")); grim_length_plot

grim_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = age_accel_grim_pregresid, y = headcirc_resids)) +
  geom_point(color = 'purple', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmGrim EEA", y = "Postnatal Head Circumference (residuals)")+
  theme_pubr()+
  annotate("text", x = -3.5, y = -4.5, label=expression(beta *  "= 0.09,  p-value = 0.086")); grim_headcirc_plot

TL_headcirc_plot <-ggplot(data = combined_data, mapping = aes(x = dn_am_tl_adj_age_pregresid, y = headcirc_resids)) +
  geom_point(color = 'darkgreen', alpha = 0.7) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "DNAmTL EEA", y = "Postnatal Head Circumference (residuals)")+
  theme_pubr()+
  annotate("text", x = -0.125, y = -4.5, label=expression(beta *  "= 0.09,  p-value = 0.086")); TL_headcirc_plot

ggarrange(grim_length_plot, grim_headcirc_plot, TL_headcirc_plot,  
          labels = c("A", "B", "C"), 
          ncol = 3, nrow = 1)